Screening mammography is a known method for detecting early signs of breast cancer in women. Generally, women undergo an X-ray exam in which X-ray films of the breast are exposed and then developed for review. A radiologist reads the films and assesses the likelihood of the presence of signs of breast cancer. If a suspicious finding is present, the woman will typically be invited for additional, more detailed diagnostic X-ray exams, followed by ultrasonic exams, and possibly biopsy.
In a typical screening exam in the United States of America, four X-rays of the breast are obtained. In conventional practice, two mammographic views are obtained for each breast: a cranio-caudal (CC) view is obtained by positioning the X-ray film horizontally under the compressed breast, and a medio-lateral oblique (MLO) view is obtained by positioning the X-ray film in a plane that is approximately orthogonal to the left-right axis. In some situations, more or fewer X-ray views may be obtained. The four views are typically labeled LCC (Left Cranio-Caudal), RCC (Right Cranio-Caudal), LMLO (Left Medio-Lateral Oblique) and RMLO (Right Medio-Lateral Oblique).
One goal of image processing of mammography images is to provide an optimal rendering of breast tissue for the diagnostician. Image data that is initially analyzed and used for this purpose can include detection of the different areas of the image data, for example: direct exposure areas, collimation areas, markers, and anatomy. An optimal tone scale can be calculated and used for display, based on characteristics of the anatomy area. For example, see Barski et. al., “New Automatic tone scale method for computed radiography,” Proc. SPIE, 3335, 164-178, 1998. Further, mammography has specific requirements regarding the appropriate display of different tissue consistencies or densities. Analysis and classification of breast density based on the breast appearance within the digital image data can provide additional information such that an optimal rendering of each mammography image can be displayed.
FIG. 1 shows four exemplary unprocessed digital views of a mammogram taken during a typical screening exam. A display 10 includes an RMLO image 20, an LMLO image 30, an RCC image 40, and an LCC image 50 arranged as shown. Each image typically has a corresponding marker 12, placed by the technician nearest the axilla of the patient prior to imaging.
Breast density has been acknowledged to be a factor in effective mammogram interpretation. For example, there is a consideration that mammographic imaging techniques are less successful with denser breast tissue than with predominantly fat tissue. Fibro-glandular tissue in the breast tends to attenuate x-rays to a greater degree than does fat tissue, leading to increased difficulty in detection of cancer sites for denser breasts. As a guideline for classification, the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BIRADS) has identified four major groupings for breast tissue density. Class I corresponds to breasts having high concentration of fat tissue. The Class II grouping indicates scattered fibroglandular densities. Class III indicates heterogeneously dense tissue. Class IV corresponds to extremely high breast density.
Various methods have been used for evaluation of breast density in mammograms. For example, Byng et al. in an article entitled “The Quantitative analysis of mammographic densities”, Phys. Med. Biol. 39, 1994, discloses a method for quantifying the breast density using an interactive thresholding technique, which assesses the proportion of the mammographic image that represents dense tissue. Zhou et. al. in “Computerized image analysis: Estimation of breast density on mammograms”, Medical Physics, 28 (6) 2001) describes a method for estimating mammographic breast density by using rule-based classification on the image gray-level histogram. Saha et al. in an article entitled “Breast tissue density quantification via digitized mammograms”, IEEE Transactions on Medical Imaging, Vol. 20, No. 8, 2001) describes a method to segment dense tissue regions from fat within breasts from mammograms using scale-based fuzzy connectivity methods; then, different measures for characterizing mammography density are computed from the segmented regions. Bovis et al. in “Classification of Mammographic Breast Density Using a Combined Classifier Paradigm”, International Workshop on Digital Mammography, p 177-180, 2002) investigated texture-based discrimination between fatty and dense breast types from the construction of spatial gray-level dependency matrices. Recently, Petroudi et al. in “Automatic Classification of Mammographic Patenchymal Patterns: A Statistical Approach”, IEEE Engineering in Medicine and Biology Society, vol. 2, p 416-423, 2003) used textons to capture the mammographic appearance within the breast area.
While these approaches address the breast density classification problem, there remains a need for improvement in automated techniques for density classification. More accurate classification results, for example, can help to optimize image display for the diagnosing physician. An incremental improvements in tissue assessment and classification can result in increased accuracy of detection in using mammography.